analytics big data salary report 2016

8/17/2019 Analytics Big Data Salary Report 2016

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ANALYTICS & BIG DATASALARY REPORT 2016A complete rundown of pay packages in the Indian industry


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© Jigsaw Academy Education Pvt Ltd 01

INDEX

Introduction................................................................................................................... 02

Executive Summary................................................................................................... 05

Note from Kunal Jain (CEO, Analytics Vidhya) ....................................... 06

Salary Spread by Tools............................................................................................. 07

Salary Spread by Tech.............................................................................................. 08

Salary Spread by City............................................................................................... 09

Salary Spread by City (Cost of Living Adjusted)...................................... 10

Salary Spread and Skills Required by Company Size............................ 11

Salary Spread by Experience............................................................................... 12

Skill Word Cloud......................................................................................................... 13

Methodology.................................................................................................................

14

Insights.............................................................................................................................. 15


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INTRODUCTION- By Gaurav Vohra, CEO, Jigsaw Academy

Growth in 2015

The Analytics & Big Data sector has seen consistent growth in the last five

years despite an increasingly uncertain global outlook. The advanced

analytics market is expected to grow at a CAGR of 33.2% and Big Data at a

CAGR of 26.4%, almost six to eight times that of the overall IT markets.

Impact on businesses

Analytics & Big Data have revolutionised the way business is done around

the world. All companies, no matter what size, rely on data and analytics to

make critical business decisions. From understanding consumer behaviour

to predicting market trends, even right down to product features, many

moves are driven by analytics and data in companies across the world.

Analytics

Big Data

Defense CrimePrevention

Governance Entertainment Education

Education Finance Transportation Retail Healthcare

Transportation

Today, Analytics is most used in



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Leading by example

Two of the most famous companies in the world use analytics and

Big Data to shape their product, services and delivery - Amazon

and Facebook.

Both companies have changed the way consumers use the internet.

Needless to say, if you found this report online, it’s because analytics & Big

Data made it possible.

Amazon uses analytics to

curate products on their

customers’ homepages based

on their previous purchases

and browsing habits.

Facebook uses analytics to fill your news

feed with updates from people you

interact with the most; content from sites

you frequent and products you have

checked out on other sites.



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Hiring trends

The days of specialists in one tool or domain are gone. Companies

today are looking for employees who know how to use the entire tool

set of analytics & Big Data. This year, we found that people who

know R and Python command a premium.

Educational qualifications required for data science have also seen a

shift. Just a Masters or a Phd on your CV is no longer impressing

companies today. They want people who have the business acumen

or at least business knowledge and understanding of analytics. This

means you don’t just need an MBA, you also need to know analytics

to get that job.

PredictiveAnalytics is used by

Flipkart, Snapdeal

and Paytm

DHL has incorporated

analytics & Big

Data into theirbusiness processes

UPS (United Postal

Service) invests in

gathering and

analysing data toimprove last mile

delivery operations



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EXECUTIVE SUMMARY

R, a crowd pleaser, remains the front-runner in

the analytics race, though Python is hot on its

heels. That said, the more varied your skill set,

the more you earn.

Analysts skilled in both Big

Data and Data Science are

being snapped up at the

highest salaries.

Surprisingly, the best

city to be an analyst in

is inexpensive Kolkata.

Analysts can expect an

astronomical increase in their

earnings once they cross the

5-year mark.

RPython

R

SQLSAS

5 YEARS

PythonR ++SAS SPSS+

Kolkata

STARTUP

Big Data +

Data Science

Cash-strapped startups spend money

on their star analysts but when it comes

to tools, they prefer to use open-source

ones like R.



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NOTE FROMTHE CEO OFANALYTICS VIDHYA


There has been no better time to

be a data science professional.

With increasing data being

generated and collected byvarious companies, the demand

for talent is greater than ever.

And this is just the beginning.

We are in phase 1 of what I

call the ‘data wave’, where

companies are creating their

own databases / data warehouses

/ data lakes, data models, and

predictive models.

The next phases of this wave will

see an influx of plug and play

data products. As the difference

between software or app

development and data science

start to blur, we will start to

see the need for ‘full stack

data developers’ emerging in

the industry.

At Analytics Vidhya, we

have interacted with more

than a million data science

professionals in the last one year

and we will continue to analyze

important trends in the industry.

This groundbreaking study was

conducted with over 60,000

analytics professionals. We are

extremely excited about

releasing some of these insightsto a larger audience, and hope to

help more and more people

choose a fulfilling career in the

wonderful world of analytics &

Big Data.

- Kunal Jain


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R skills pay the most and are used the most. But relying on just one tool is not going to

cut it, as the data shows that the highest salaries are paid to analysts who know more

than just R or Python.

Even in multitool skill sets, combinations that include R and Python are best paid.

The highest recorded salary for 2015 - 2016 is 12.75 lakhs p.a. for analysts with the

knowledge of SPSS + SAS + R + Python.

8.01L

8.54L

9.36L

10.2L

10.96L

11.48L

11.95L

12.75L

SPSS

SPSS

SAS

SAS

SAS

Python PythonR

R+

Python

R+

+

Python

R+

+SPSS

Python

R+

+

+

SALARY SPREAD BY TOOLS

Learn more to earn more

You can makea round trip from

Bombay to the Bay Area every month! S a l a r y

i n l

a k h s p . a .



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Data Science9.5 L

Data Science

+ Big Data

13.1 L

9.8 LBig Data

SALARY SPREAD BY TECH

The power of Big Data is strong with this one.

Although Big Data professionals earn more than data scientists, the larger payout is

in knowing how to work with both - data science & Big Data technologies. To be

precise, we mean 26% more.

One is not enough

S a

l a r y

i n l

a k h s p . a .

Buy yourself a new

iPad Pro every month!



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SALARY SPREAD BY CITY

Mumbai pays the highest among all cities in absolute numbers at 12.19 lakhs p.a. but it is

also the most expensive city to live in.

However, once the cost of living is factored in, things look different!

12.19 LMumbai

10.48 LBangalore

10.4 LDelhi NCR

9.81 LPune

9.45 LChennai

9.42 LHyderabad

9.35 LKolkata

S a l a r y

i n l

a k h s p . a .

Absolute Salaries

Maximum city pays the maximum money



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Live life kingsize in Kolkata

9.75 LMumbai

10.48 LBangalore

9.37 LDelhi NCR

10.44 LPune

10.27 LChennai

10.24 LHyderabad

10.51 LKolkata

S a l a r y

i n l

a k h s p . a .

SupplyLow

DemandLow

SupplyHigh

DemandHigh

SupplyLow

DemandLow

SupplyLow

DemandMedium

SupplyLow

DemandMedium

SupplyMedium

DemandHigh

SupplyHigh

DemandHigh

SALARY SPREAD BY CITY(Cost of Living Adjusted)

After factoring in the cost of living, Kolkata emerges as the best paying city to live in.

Cost of Living Adjusted Salaries

Get yourselfa new iPhone6S Plus every

month!



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S a

l a r y

i n l

a k h s p . a .

Mid levelCompany

10.2 L

Startup

10.8 L

LargeCompany

9.6 L

Start small to earn big

Upgrade your

ride with a new

Harley Davidson

every year!

No ofemployees

Startup 1000

Revenue

< 30 Crores

30 - 1000 Crores

> 1000 CroresPopular

Tools

RSQL

HADOOP

PopularTools

RSAS

SQL

PopularTools

R

SAS

Teradata

SALARY SPREAD ANDSKILLS REQUIRED BY COMPANY SIZE

Startups need people who know R and they are willing to pay top dollar for it. 10.8 lakhsp.a. by startups is a significant step up from the 9.6 lakhs p.a paid by larger companies.

Even mid-sized companies are paying more than the large companies on average.

R is in great demand across the board. But, if you want to join a larger company, you need

to add SAS to your skill set. This is because bigger companies can afford to pay for the

more expensive enterprise versions of proprietary softwares like SAS.



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The biggest jump in salaries is seen after the 5-year mark, where analysts can expect up

to a 70% raise with an average pay of 12.3 lakhs p.a. A decade of experience can boost

this figure by more than 250%.

SALARY SPREAD BY EXPERIENCE

Do the time, see the dime

12.3 L

5-7years

18.1 L

7-10years

>30 L10 +years

70 %Jump70 %Jump

Be a regularfor a 5-star

dining experience at

The Taj every day!

7.1 L

3-5years

0 - 3years

5.9 L S a l a r y

i n l

a k h

s p . a .



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BigData

Regression

SupervisedLearning

DataAnalytics

Pig

Excel Visualization

Neural

Network

Matlab

R

SPSS SQL

Python

Hive

SAS

Statistic

DataMining

MapReduce

Clustering

DataModeling

MachineLearning

Hadoop

Analytics

ArtificialIntelligence

mongodb

SKILL WORD CLOUD

The word cloud is based on profile tags of analytics professionals who were part of the

study. These tags were added by users themselves or were mined from their CV. Thediagram represented below shows the distribution of the tags so collected.



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METHODOLOGY

The data points include 60,000+ people who contacted the

Analytics Vidhya team through their website, via job platforms

and other sources. This number also includes but is not limited

to people applying for jobs on the Analytics Vidhya website,

confidential searches, hackathons and tie-ups with skill

enhancement partners.

People with only advanced analytics / data science skills were

considered for the study. For example, people with entire

experience in MIS have been excluded from the study. Oncefiltered, the skill sets of the survey participants were

appropriately categorized in order to gain insights into the data.

The findings are open to biases arising because of the nature of

jobs / competitions hosted on Analytics Vidhya’s website.

However we believe that even with this bias, this

first-of-its-kind study of the Indian Analytics industry and the

thousands of analytics professionals it employs, reveals many

fascinating ground realities.



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INSIGHTS

Three Tips for Anyone Consideringa Career in Data Analytics

Ask yourself if you’re temperamentally

suited to being a data scientist for the

long haul. Data analysts have an

investigative bent of mind and enjoy

solving puzzles. They also spend

large chunks of time on their own,

adrift in a sea of numbers, trawling

and inspecting huge caches of

random data to see if they can

discern any meaningful patterns or

telling incongruities.

It doesn’t matter what our educational background is, we

can all crack analytics. I did it as a commerce graduate. If

you have an interest in data and want to transition to an analytics

role, believe in yourself and make it happen.” Clarence Wong, Jigsaw

Academy graduate, Architect at Data Science at ValueLabs, Hyderabad

As Frank Coleman,

who is Director ofBusiness Operations for EMC Global

Services puts it, “[…] as I recruit

talent and continue to build my

teams, I’m not just looking for

technical skills; I’m also looking

for the right personalities.”1

TIP 1

Do an honest self-assessment

Perhaps you have discovered a passion for analytics at work, and want to

acquire a more in-depth understanding of specific analytics software. Or

maybe you want to switch careers altogether, and do work that’s

analytics-intensive. Whatever your motivation, here are a couple of suggestions to

help you make an informed foray into this field.



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Figure out if you want to go back to college to get a degree in data science, or if doing

an intensive certification course is more pragmatic. Do some reconnaissance to

figure out which universities/institutes are best suited to your needs.

Analytics comprises varied techniques and tools that can be deployed in different

permutations and combinations, for purposes as diverse as business intelligence and healthcare

management. Your intended work ‘domain’ will dictate the kind of courses you take. Also keep in

mind that certain software skills are in greater demand in some professions/sectors than others.

Clarence Wong picked a course from Jigsaw Academy because he

felt “that they were most application-oriented and their

curriculum was flexible, when compared with certification programmes at

IIM, ATI and NIIT Imperia.”

TIP 2

Familiarise yourself with

the data analytics landscape

TIP 3

Plot a course of action

In Clarence Wong’s case2, though he used some basicanalytics at his job in HSBC’s anti-fraud division, his interest

kicked into high gear only after he joined Google. He started using

techniques like clustering and decision trees, along with software like

R and Python. This got him to seriously consider a course in analytics.

1 https://infocus.emc.com/frank_coleman/6-personality-traits-your-data-science-team-needs/

2 http://analyticstraining.com/2014/is-analytics-only-for-people-with-tech-backgrounds/



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Most In-Demand DataScience Skill sets

The median

salary for Big

Data professionals is six figures

and comes to about $104,850.

Big Data

R

While data analytics skills are in great demand these days,

some skills are more in demand than others. Your

hands-on experience with different kinds of software will

help command better salaries than expertise in just one.

Sometimes referred to as ‘a hyperactive version of excel’, R is used by organisations as

varied as Facebook, Google, Bank of America, and The New York Times 3. It’s used to sift

through gargantuan data sets, that it can then easily ‘manipulate’ using modelling

techniques and powerful data visualisation tools4.

R is also proving its mettle in the world of Machine Learning, which, according to Google’sEric Schmidt is “the technology that enables computers to get smarter and

more personal”5, which is where he believes the future of his company lies.

The following skills and specialisations were featured the most in 2015’s job listings.

Research shows that data scientists familiar with both R and

Python make close to 1 lakh more than analysts who only know R,

and nearly 2 lakhs more than analysts who only know Python1.

According to Computer Business Review

Online, Big Data was cited 112,469

times between January and June 20152

in the classifieds - garnering 118% more

mentions than it received during the

same period in 2014.

Popular Big Data-specific skills include statistics, programming, and mathematical

modelling. A combined knowledge of R and Python, for example, can equip you with these.



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1 Reference our Analytics & Big Data Salary Report 2016

2 http://www.cbronline.com/news/big-data/software/big-data-jobs-5-most-in-demand-skills-4739437

3 http://www.fastcompany.com/3030716/the-9-best-languages-for-crunching-data

4 http://www.infoworld.com/article/2940864/application-development/r-programming-

language-statistical-data-analysis.html5 http://www.analyticsvidhya.com/blog/2015/08/common-machine-learning-algorithms/

6 http://blog.codeeval.com/codeevalblog/2015#.VqC4LCp97IU=

7 http://www.inc.com/larry-kim/10-most-popular-programming-languages-today.html

Python

Codeval.com rated Python the number 1 coding language in the world, for the fourth

year in a row, in February 20156. Developed by Guido Van Rossum in the mid-90s,

Python is a versatile, open-source programming language and framework

that Google has utilised and invested in quite heavily, ensuring its

widespread use and popularity.

Touted as the ‘easiest programming language to learn’7, Python’s framework can

be used not just to create web apps (like Pinterest and Instagram), but also to

perform analytics.

Hadoop

Like Big Data, Hadoop was referenced 31,274 times in job advertisements, an

increase of 118% over the previous year’s mentions. With its capacity for computing

Big Data on a large scale, Hadoop’s appeal shows no sign of diminishing.

Conclusion

If the future is bright for Big Data scientists, it is especially

radiant for professionals who can demonstrate their mastery

over diverse programming and statistical frameworks.

It is expected to demonstrate a compound annual growth rate of 58.2%

between 2013 and 2020.



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The biopharmaceutical sector accounts for “about 85% of the PST

sector’s total R&D spending.”2 Much of this goes towards building Big

Data and analytics capacities (especially predictive), in areas such as

CRM (customer relationship management) and R&D Informatics.”3

Biopharmaceuticals (PST)

Demand for Big Data has diversified, and is no longer limited to

just a handful of industries like a few years ago. According to

experts, this is because small and medium-size companies havealso started to hire data analysts in diverse industries.

Three Industries That Aspiring DataScientists Should Target in 2016

The data revolution of the late 20th and

early 21st centuries changed how

organisations marshal, manage, utilise

and interact with electronic information.

If Big Data is the digital equivalent of oil, then analytics frameworks are the machinery

used to tap, siphon, and convert ‘crude’ into info-fuel. Given this scenario, individuals withthe technical expertise to facilitate such a complex process are in great demand.

So who exactly is hiring these experts? “Well, who isn’t?!” would be the best

answer to that question!

These are the top five industries driving the demand for data science skills in 2015

(experts expect this trend to continue in 2016 as well1)

• Professional, Scientific,

and Technical Services (PST)

• Information Technologies (IT)

• Manufacturing

• Finance and Insurance

• Retail

Let’s take a closer look at the top three industries, particularly key segments:



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Bio-pharma is growing with incredible speed in India. This industry, which entails vaccine

development, therapeutics and diagnostics is Indian pharma’s most lucrative sub sector.

Bio-pharma generates 62% of the PST sector’s total revenue, which amounts to

$1.9 billion or Rs 12, 600 crore a year.4

In tandem with the Indian Bio-IT sector, which offers data analytics “solutions through

contract research organisations (CROs)”5 to various companies around the world, it is

clear that there is an escalating need for talented data scientists in the Indian

pharmaceutical field.

As a generation of internet users accesses the web solely through WHDs (wireless

handheld devices), cybersecurity solutions driven by predictive analytics will gain

tremendous traction.6

Neutralising cyber threats through the use of predictive analytics is helping

security professionals identify and track cyber-crime in real time.

Todd Pedersen, a cybersecurity expert at CSC confirms this shift, saying, "We used to

make statements, such as 'I have a firewall; I'm protected,' or 'I have antivirus software;I'm protected,'" "Now, the conversation is less about preventing an attack, threat or

exposure, and more about how quickly you can detect that an attack is happening."7 This

makes cybersecurity an industry worth targeting, and not just for cyber criminals.

Cybersecurity (IT)

Manufacturing

From machine utilisation

to demand forecasts to

quality improvement, Big Data analytics

is proving to be indispensable to the

manufacturing sector.

The advent of e-commerceand the growth of the digital

marketplace have changed

the way manufacturing

works. They have rendered

the boundaries between

companies and their customers

increasingly porous, making

it easier for businesses to predict the likelihood of customer demands (forecasts) for

specific products.



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Given the limited presence of talented data scientists, both globally and in India 9, and

soaring cross-sectoral demand—especially in the fields outlined above—individuals with

the right skills are truly spoiled for choice.

Conclusion

The capacity to gauge customer demand, using Big Data and advanced analytics, allows

companies to calibrate production accordingly. Analytics can also improve critical

components of the production process, such as machine utilisation, by increasing

‘machine uptime’ or functioning.8


1 http://www.forbes.com/sites/louiscolumbus/2015/11/16/where-big-data-

jobs-will-be-in-2016/#58409693f7f1

2 http://www.iriweb.org/sites/default/files/2016GlobalRDFundingForecast.pdf

3 ibid

4 htt p://www.ibef.org/industry/pharmaceutical -india.aspx

5 https://www.outsource2india.com/kpo/articles/bio-it.asp

6 http://www.techrepublic.com/blog/10-things/10-predictions-for-the-it-job-market-in-2016/

7 http://www.csc.com/cybersecurity/publications/93325/104033-using_big_data

_to_defend_against_cyber_security_threats

8 By “comparing past machine failures to sensor data from the machines to identify patterns

before breakdowns occur” http://cerasis.com/2015/12/07/6-manufacturing-trends -to-watch-out-for-in-2016/

9 By 2018 India will be short of 2 lakh data scientists


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Mark Twain once wrote, “Don’t let school interfere with

your education.” What he meant to say (in part) was that

true learning doesn’t stop when we graduate. This is as

true for graduates as it is for seasoned professionals.

Nowhere is this more pertinent than in data analytics. As ICTs evolve and generate newer,

more affordable solutions for persistent problems, practitioners will have to keep pace if

they want to experience career growth and remain relevant.

There are, however, certain so-called ‘soft skills’—professional traits and habits—that can

burnish an analyst’s existing credentials. Here are some of them:

As Ed Burns, editor of SearchBusinessAnalytics puts it, “It would be best to proactively

acquire the skills necessary for dealing with data now rather than wait for your employer

to decide that these skills are a core competency of your job1.” If this applies to individuals

without a data science background, it is doubly important for analytics professionals, who

run the risk of complacency.

Don’t rest on your laurels—add to them.

If your skills are no longer relevant, no matter which way you slice it,

you’re like a chef with a blunt knife—ineffectual.

Keep track of market trends and actively seek out opportunities to

expand your data skillsets.

Being proactive

Data Analysts Should DevelopMore Than Just Their Technical

Skills. Here’s Why.



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1 http://searchbusinessanalytics.techtarget.com/opinion/Take-your-data-analysis-skills-to-the-next-level

Writing up and presenting your insights with clarity,

and articulating data-based inferences/findings with style, adds tremendous value to the work you produce.

If you master both the ‘what’ and the ‘how’ aspects

of analytics, you can bridge the gap between BI

(business intelligence) and IT.

Being ‘presentable’

Learning to liaise

Beefing up your core competencies involves more than just learning how to use the latest

and most lucrative skills; it entails the ability to formulate and effectively communicate

the results of your analytics work.

Often, professionals who are knowledgeable about IT, are clueless about business

strategizing and development. If you can serve as a conduit between these two nervecentres, and create a synergy between them, you will become indispensable.

Attending industry events

Attending conferences and events is a great way to

network, gauge emerging trends, and learn from

peers and thought leaders.

Kaggle2 meets make a great addition to your resume, and give you an opportunity to put

your analytics skills to the test. Since participants’ performances are rated, future

employers have a way to objectively gauge their skill range and levels.

Employees who evolve along with the market, and who take the initiative to pursue a

range of technical and non-technical skills, are priceless. Their high salaries are just an

industry’s way of scrambling to put a price on them.

analytics big data salary report 2016

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